41–50 of 53 posts
I guess it will be this year, some guy working at OpenAI already posted "4+1=5" on Twitter, which is suggestive.
I believe that gpt-4o was really gpt-5, just renamed, and the multimodal stuff they demoed will actually be released within a month.
GPT-4o feels GPT5ish to me. It's crazy fast and doesn't make the stupid mistakes 3 and 4 did. Also it doesn't hallucinate nearly as much and its inference capability is impressive. I can be rather vague in my question and it typically understands what I am getting at.
It was a really eye-opening experience.
What's not 'safe' about AI? If you mean the hallucinations I don't think that will ever really be solved. I think people just have to learn that LLMs are not divine oracles that are always correct. Just like the training data generated by the flawed humans that are often either wrong or outright lying. Garbage in, garbage out. Not saying that AI isn't useful. But expecting what is basically a "human simulator" not to…
> If you mean the hallucinations I don't think that will ever really be solved. This is true by definition, since a 'hallucination' isn't a failure condition in which the system isn't working as designed, it's just a post-hoc term for when probabilistic output doesn't meet our expectations, which is inherent to the nature of all probabilistic systems. Within the system, there is no distinction between 'hallucination'…
This seems impossible to me. Many of the tasks I use GPT for inherently require understanding and thinking.
Everyone keeps thinking current GPT models will improve to be superhuman, it won't. It's trained on human data. Much like alpha-go had to drop completely the concept of learning from human plays since it was stuck at a local minimum. Once they started to train with adversarial networks, it evolved a well above the previous local minimum (with an extra order of magnitude of computation). So, don't expect much more fro…
Earlier quoted context omitted.
> If you mean the hallucinations I don't think that will ever really be solved. This is true by definition, since a 'hallucination' isn't a failure condition in which the system isn't working as designed, it's just a post-hoc term for when probabilistic output doesn't meet our expectations, which is inherent to the nature of all probabilistic systems. Within the system, there is no distinction between 'hallucination'…
>have no awareness of the semantic meaning of the tokens they correlate This seems impossible to me. Many of the tasks I use GPT for inherently require understanding and thinking.
The later iterations are heavily censored so the public was provided a bit of a transition period before things got too chaotic.
I'm sure there were many other reasons the authors themselves weren't aware of at the time such as the inundation of AI content skewing further training quality.
Of course this is a roundabout explanation, there's always more detail that can be added and I'd rather be objective. There's always a financial motive for companies too so take that into consideration. The hype definitely played into their marketing.
I predict - a steady increment of GPT-n+1 every 6 months for marketing purposes. - each will improve on the last by smaller and smaller margins. - hallucinations won't be fixed anytime soon. - We will hit a bit of a winter, as the hype was so big but like self driving cars the devil is in the details. The general public realizes these things are just essentially giving us averages. - A big market will emerge around "…
> - a steady increment of GPT-n+1 every 6 months for marketing purposes. You already failed then, gpt4 was released more than a year ago
Earlier quoted context omitted.
>have no awareness of the semantic meaning of the tokens they correlate This seems impossible to me. Many of the tasks I use GPT for inherently require understanding and thinking.
It is kinda hard to grasp but an llm does not 'think'. It basically predicts the next letter in a sentence and it's quite efficient at it.
Earlier quoted context omitted.
>have no awareness of the semantic meaning of the tokens they correlate This seems impossible to me. Many of the tasks I use GPT for inherently require understanding and thinking.
It is kinda hard to grasp but an llm does not 'think'. It basically predicts the next letter in a sentence and it's quite efficient at it.
Earlier quoted context omitted.
GPT-4o feels GPT5ish to me. It's crazy fast and doesn't make the stupid mistakes 3 and 4 did. Also it doesn't hallucinate nearly as much and its inference capability is impressive. I can be rather vague in my question and it typically understands what I am getting at.
I tried learning Godot with 4o for a few days and almost literally every answer was hallucinated and wrong. Didn't matter if I specified the version of Godot I was using among other specifics. It would even repeat mistakes that I had corrected earlier in the conversation (and it claimed to "save to memory"). It was a really eye-opening experience.
So it's still not perfect :)